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1 ESTIMATING THE PAYOFF TO ATTENDING A MORE SELECTIVE COLLEGE: AN APPLICATION OF SELECTION ON OBSERVABLES AND UNOBSERVABLES* STACY BERG DALE AND ALAN B. KRUEGER Estimates of the effect of college selectivity on earnings may be biased because elite colleges admit students, in part, based on characteristics that are related to future earnings. We matched students who applied to, and were accepted by, similar colleges to try to eliminate this bias. Using the College and Beyond data set and National Longitudinal Survey of the High School Class of 1972, we nd that students who attended more selective colleges earned about the same as students of seemingly comparable ability who attended less selective schools. Children from low-income families, however, earned more if they attended selective colleges. A burgeoning literature has addressed the question, Does the quality of the college that students attend in uence their subsequent earnings? 1 Obtaining accurate estimates of the payoff to attending a highly selective undergraduate institution is of obvious importance to the parents of prospective students who foot the tuition bills, and to the students themselves. In addition, because college selectivity is typically measured by the average characteristics (e.g., average SAT score) of classmates, the literature is closely connected to theoretical and empirical studies of peer group effects on individual behavior. And with higher education making up 40 percent of total educational expenditures in the United States (see U. S. Department of Education [1997, Table 33]), understanding the impact of selective colleges on students labor market outcomes is central for understanding the role of human capital. 2 * We thank Orley Ashenfelter, Marianne Bertrand, William Bowen, David Breneman, David Card, James Heckman, Bo Honore, Thomas Kane, Lawrence Katz, Deborah Peikes, Michael Rothschild, Sarah Turner, colleagues at the Mellon Foundation, and three anonymous referees for helpful discussions. We alone are responsible for any errors in computation or interpretation that may remain despite their helpful advice. This paper makes use of the College and Beyond (C&B) database. The C&B database is a restricted access database. Researchers who are interested in using the database may apply to the Andrew W. Mellon Foundation for access. 1. The modern literature began with papers by Hunt [1963], Solmon [1973], Wales [1973], Solmon and Wachtel [1975], and Wise [1975], and has undergone a recent renaissance, with papers by Brewer and Ehrenberg [1996], Behrman et al. [1996], Daniel, Black, and Smith [1997], Kane [1998], and others. See Brewer and Ehrenberg [1996, Table 1] for an excellent summary of the literature. 2. This gure ignores any earnings students forgo while attending school, which would increase the relative cost of higher education by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. The Quarterly Journal of Economics, November

2 1492 QUARTERLY JOURNAL OF ECONOMICS Past studies have found that students who attended colleges with higher average SAT scores or higher tuition tend to have higher earnings when they are observed in the labor market. Attending a college with a 100 point higher average SAT is associated with 3 to 7 percent higher earnings later in life (see, e.g., Kane [1998]). As Kane notes, an obvious concern with this conclusion is that students who attend more elite colleges may have greater earnings capacity regardless of where they attend school. Indeed, the very attributes that lead admissions committees to select certain applicants for admission may also be rewarded in the labor market. Most past studies have used Ordinary Least Squares (OLS) regression analysis to attempt to control for differences in student attributes that are correlated with earnings and college qualities. But college admissions decisions are based in part on student characteristics that are unobserved by researchers and therefore not held constant in the estimated wage equations; if these unobserved characteristics are positively correlated with wages, then OLS estimates will overstate the payoff to attending a selective school. Only three previous papers that we are aware of have attempted to adjust for selection on unobserved variables in estimating the payoff to attending an elite college. Brewer, Eide, and Ehrenberg [1999] use a parametric utility maximizing framework to model students choice of schools, under the assumption that all students can attend any school they desire. Behrman, Rosenzweig, and Taubman [1996] utilize data on female twins to difference out common unobserved effects, and Behrman et al. [1996] use family variables to instrument for college choice. Our paper complements these previous approaches. This paper employs two new approaches to adjust for nonrandom selection of students on the part of elite colleges. In one approach, we only compare college selectivity and earnings among students who were accepted and rejected by a comparable set of colleges, and are comparable in terms of observable variables. In the second approach, we hold constant the average SAT score of the schools to which each student applied, as well as the average SAT score of the school the student actually attended, the student s own SAT score, and other variables. The second approach is nested in the rst estimator. Conditions under which these estimators provide unbiased estimates of the payoff to college quality are discussed in the next section. In short, if admission to a college is based on a set of variables that are

3 ATTENDING A MORE SELECTIVE COLLEGE 1493 observed by the admissions committee and later by the econometrician (e.g., student SAT), and another set of variables that is observed by the admissions committee (e.g., an assessment of student motivation) but not by the econometrician, and if both sets of variables in uence earnings, then looking within matched sets of students who were accepted and rejected by the same groups of colleges can help overcome selection bias. Barnow, Goldberger, and Cain [1981] point out that, Unbiasedness is attainable when the variables that determined the assignment rule are known, quanti ed, and included in the [regression] equation. Our rst estimator extends their concept of selection on the observables to selection on the observables and unobservables, since information on the unobservables can be inferred from the outcomes of independent admission decisions by the schools the student applied to. The general idea of using information re ected in the outcome of independent screens to control for selection bias may have applications to other estimation problems, such as estimating wage differentials associated with working in different industries or sizes of rms (where hiring decisions during the job search process provide screens) and racial differences in mortgage defaults (where denials or acceptances of applications for loans provide screens). 3 We provide selection-corrected estimates of the payoff to school quality using two data sets: the College and Beyond Survey, which was collected by the Andrew W. Mellon Foundation and analyzed extensively in Bowen and Bok [1998], and the National Longitudinal Survey of the High School Class of 1972 (NLS-72). Two indirect indicators of college quality are used: college selectivity, as measured by a school s average SAT score, and net tuition. Our primary nding is that the monetary return to attending a college with a higher average SAT score falls considerably once we adjust for selection on the part of the college. Nonetheless, we still nd a substantial payoff to attending schools with higher net tuition. Although most of the previous literature has implicitly assumed that the returns to attending a selective school are homogeneous across students, an important issue in interpreting our ndings is that there may be heterogeneous returns to students 3. Braun and Szatrowski [1984] use a related idea to evaluate law school grades across institutions by comparing the performance of students who were accepted at a common set of law schools but attended different schools.

4 1494 QUARTERLY JOURNAL OF ECONOMICS for attending the same school. Some students may bene t more from attending a highly selective (or unselective) school than others. For example, a student intent on becoming an engineer is likely to have at least as high earnings by attending Pennsylvania State University as Williams College, since Williams does not have an engineering major. In this situation, if students are aware of their own potential returns from each school to which they are admitted, they could be expected to sort into schools based on their expected utility from attending that school, as in the Roy model of occupational choice. In other words, the students who chose to go to less selective schools may do so because they have higher returns from attending those schools (or because there are nonpecuniary bene ts from attending those schools); however, the average students might not have a higher return from attending a less selective school over a more selective one. Nonetheless, contrary to the previous literature, this interpretation implies that attending a more selective school is not the income-maximizing choice for all students. Instead, students would maximize their returns by attending the school that offers the best t for their particular abilities and desired future eld of employment. I. A STYLIZED MODEL OF COLLEGE ADMISSIONS, ATTENDANCE, AND EARNINGS For most students, college attendance involves three sequential choices. First, a student decides which set of colleges to apply to for admission. Second, colleges independently decide whether to admit or reject the student. Third, the student and her parents decide which college the student will attend from the subset of colleges that admitted her. To start, we consider a highly stylized model of both admissions and the labor market as a benchmark for analysis. We discuss departures from these simplifying assumptions later on. Assume that colleges determine admissions decisions by weighing various attributes of students. A National Association for College Admission Counseling [1998] survey, for example, nds that admissions of cers consider many factors when selecting students, including the students high school grades and test scores, and factors such as their essays, guidance counselor and teacher recommendations, community service, and extracurricular activities. Next, we assume that each college uses a threshold

5 ATTENDING A MORE SELECTIVE COLLEGE 1495 to make admissions decisions. An applicant who possesses characteristics that place him or her above the threshold is accepted; if not, he or she is rejected. Additionally, idiosyncratic luck may enter into the admission decision. The characteristics that the admissions committee observes and bases admission decisions on can be partitioned into two sets of variables: a set that is subsequently observed by researchers and a set that is unobserved by researchers. The observable set of characteristics includes factors like the student s SAT score and high school grade point average (GPA), while the unobservable set includes factors like assessments of the student s motivation, ambition, and maturity as re ected in her essay, college interview, and letters of recommendation. For simplicity, assume that X 1 is a scalar variable representing the observable characteristics the admissions committee uses and X 2 is an unobservable (to the econometrician) variable that also enters into the admissions decisions. 4 We assume that each college, denoted j, uses the following rule to admit or reject applicant i: (1) if Z ij 5 1X 1i 1 2X 2i 1 e ij. C j then admit to college j otherwise reject applicant at college j, where Z ij is the latent quality of the student as judged by the admissions committee, e ij represents the idiosyncratic views of college j s admission committee, 2 and 2 are the weights placed on student characteristics in admission decisions, and C j is the cutoff quality level the college uses for admission. 5 The term e ij represents luck and idiosyncratic factors that affect admission decisions but are unrelated to earnings. We assume that e ij is independent across colleges. By de nition, more selective colleges have higher values of C j. Now suppose that the equation linking income to the students attributes is (2) ln W i SAT j* 1 2X 1i 1 3X 2i 1 i, where SAT j* is the average SAT score of matriculants at the college student i attended, X 1 and X 2 are the characteristics used 4. In terms of the previously de ned sets of variables, one could think of X 1 and X 2 as a linear combination of the variables in each set, where the weights were selected to give X 1 and X 2 the coef cients in equation (1). 5. We ignore the possibility of wait listing the student.

6 1496 QUARTERLY JOURNAL OF ECONOMICS by the admission committee to determine admission, and i is an idiosyncratic error term that is uncorrelated with the other variables on the right-hand side of (2). Since individual SAT scores are a common X 1 variable, SAT j* can be thought of as the mean of X 1 taken over students who attend college j*. The parameter 1, which may or may not equal zero, represents the monetary payoff to attending a more selective college. This coef cient would be greater than zero if peer groups have a positive effect on earnings potential, for example. In practice, researchers have been forced to estimate a wage equation that omits X 2 : (3) ln W i SAT j* 1 92X 1i 1 u i. Even if students randomly select the college they attend from the set of colleges that admitted them, estimation of (3) will yield biased and inconsistent estimates of 1 and 2. Most importantly for our purposes, if students choose their school randomly from their set of options, the payoff to attending a selective school will be biased upward because students with higher values of the omitted variable, X 2, are more likely to be admitted to, and therefore attend, highly selective schools. Since the labor market rewards X 2, and school-average SAT and X 2 are positively correlated, the coef cient on school-average SAT will be biased upward. The coef cient on X 1 can be positively or negatively biased, depending on the relationship between X 1 and X 2. Also notice that the greater the correlation between X 1 and X 2, the lesser the bias in 91. Formally, the coef cient on school-average SAT score is biased upward in this situation because E(ln W i u SAT j*,x 1i ) SAT j* 1 2X 1i 1 E(u i u X 1i, 1X 1i 1 2X 2i 1 e ij*. C j* ). The expected value of the error term (u i ) is higher for students who were admitted to, and therefore more likely to attend, more selective schools. 6 If, conditional on gaining admission, students choose to attend schools for reasons that are independent of X 2 and, then students who were accepted and rejected by the same set of schools would have the same expected value of u i. Consequently, our proposed solution to the school selection problem is to include an unrestricted set of dummy variables indicating groups of stu- 6. A classic reference on selection bias is Heckman [1979].

7 ATTENDING A MORE SELECTIVE COLLEGE 1497 dents who received the same admissions decisions (i.e., the same combination of acceptances and rejections) from the same set of colleges. Including these dummy variables absorbs the conditional expectation of the error term if students randomly choose to attend a school from the set of schools that admitted them. Moreover, even if college matriculation decisions (conditional on acceptance) are related to X 2, controlling for dummies indicating whether students were accepted and rejected by the same set of schools absorbs some of the effect of the unobserved X 2. To see why controlling for dummies indicating acceptance and rejection at a common set of schools partially controls for the effect of X 2, consider two colleges that a subset of students applied to with admission thresholds C 1, C 2. College 2 is more selective than college 1. If the selection rule in equation (1) did not depend on a random factor, then it would be unambiguous that students who were admitted to college 1 and rejected by college 2 possessed characteristics such that C 1, 1X 1 1 2X 2, C 2. As C 1 approaches C 2, the (weighted) sum of the students observed and unobserved characteristics becomes uniquely identi ed by observations on acceptance and rejection decisions. 7 Because X 1 is included in the wage equation, the omitted variables bias would be removed if ( 1X 1 1 2X 2 ) were held constant. If enough accept and reject decisions over a ne enough range of college selectivity levels are observed, then students with a similar history of acceptances and rejections will possess essentially the same average value of the observed and unobserved traits used by colleges to make admission decisions. Thus, even if matriculation decisions are dependent on X 2, we can at least partially control for X 2 by grouping together students who were admitted to and rejected by the same set of colleges and including dummy variables indicating each of these groups in the wage regression. Notice that to apply this estimator, it is necessary for students to be accepted by a diverse set of schools and for some of those students to attend the less selective colleges and others the more selective colleges from their menu of choices. If the admission rule used by colleges depended only on X 1, and if X 1 were included in the wage equation, we would have a case of selection on the observables (see Barnow, Cain, and 7. Dale and Krueger [1999] provide a set of simulations to illustrate these results. The fact that idiosyncratic factors affect colleges admissions decisions through e ij complicates but does not distort this result.

8 1498 QUARTERLY JOURNAL OF ECONOMICS Goldberger [1981]). In this case, however, we have selection on the observables and unobservables since X 2i and e ij are also inputs into admissions decisions. Nonetheless, we can control for the bias due to selective admissions by controlling for the schools at which students were admitted. In reality, all students do not apply to the same set of colleges, and it is probably unreasonable to model students as randomly selecting the school they attend from the ones that accepted them. A complete model of the two-sided selection that takes place between students and colleges is beyond the scope of the current paper, but it should be stressed that our selection correction still provides an unbiased estimate of 1 if students school enrollment decisions are a function of X 1 or any variable outside the model. The critical assumption is that students enrollment decisions are uncorrelated with the error term of equation (2) and X 2. If the decision rule students use to choose the college they attend from their set of options is related to their value of X 2, then the bias in the within-matched-applicant model depends on the coef- cient from a hypothetical regression of the average SAT score of the school the student attends on X 2, conditional on X 1 and dummies indicating acceptance and rejection from the same set of schools. It is possible that selection bias could be exacerbated by controlling for such matched-applicant effects. Griliches [1979] makes this point in reference to twins models of earnings and education. In the current context, however, if students apply to a ne enough range of colleges, the accept/reject dummies would control for X 2, and the within-matched-applicant estimates would be unbiased even if college choice on the part of students depended in part on X 2. Also notice that it is possible that the effect of attending a highly selective school varies across individuals. If this is the case, equation (2) should be altered to give an i subscript on the coef cient on SAT. Students in this instance would be expected to sort among selective and less selective colleges based on their potential returns there, assuming that they have an idea of their own personalized value of 1i. In such a situation, our estimate of the return to attending a selective school can be biased upward or downward, and it would not be appropriate to interpret our estimate of 1 as a causal effect for the average student. Another factor that would be expected to in uence student matriculation decisions is nancial aid. By de nition, merit aid is

9 ATTENDING A MORE SELECTIVE COLLEGE 1499 related to the school s assessment of the student s potential. Past studies have found that students are more likely to matriculate to schools that provide them with more generous nancial aid packages (see, e.g., van der Klaauw [1997]). If more selective colleges provide more merit aid, the estimated effect of attending an elite college will be biased upward because relatively more students with higher values of X 2 will matriculate at elite colleges, even conditional on the outcomes of the applications to other colleges. The relationship between aid and school selectivity is likely to be quite complicated, however. Breneman [1994, Chapter 3], for example, nds that the middle ranked liberal arts colleges provide more nancial aid than the highest ranked and lowest ranked liberal arts colleges. If students with higher values of X 2 are more likely to attend less selective colleges because of nancial aid, the selectivity bias could be negative instead of positive. Finally, an alternative though related approach to modeling unobserved student selection is to assume that students are knowledgeable about their academic potential, and reveal their potential ability by the choice of schools they apply to. Indeed, students may have a better sense of their potential ability than college admissions committees. To cite one prominent example, Steven Spielberg was rejected by both the University of Southern California and the University of California Los Angeles lm schools, and attended California State Long Beach [Grover 1998]. It is plausible that students with greater observed and unobserved ability are more likely to apply to more selective colleges. In this situation, the error term in equation (3) could be modeled as a function of the average SAT score (denoted AVG) of the schools to which the student applied: u i AVG i 1 v i. If v i is uncorrelated with the SAT score of the school the student attended, we can solve the selection problem by including AVG in the wage equation. We call this approach the self-revelation model because individuals reveal their unobserved quality by their college application behavior. When we implement this approach, we include dummy variables indicating the number of schools the students applied to (in addition to the average SAT score of the schools), because the number of applications a student submits may also reveal unobserved student traits, such as their ambition and patience. Notice that the average SAT score of the schools the student applied to, and the number of applications they submitted, would be absorbed by including unrestricted

10 1500 QUARTERLY JOURNAL OF ECONOMICS dummies indicating students who were accepted and rejected by the same sets of schools; therefore, the self-revelation model is nested in our rst model. It is useful to illustrate the difference between the matched applicant model and the self-revelation models with an example. In the matched-applicant model, we compare two students who were each accepted by both a highly selective college, such as the University of Pennsylvania, and a moderately selective college, such as Pennsylvania State University, but one student chose to attend Penn and the other Penn State. It is possible that the reason the student chose to attend Penn State over Penn (or vice versa) is also related to that student s earnings potential: those who chose to attend a less selective school from their options may have greater or lower earnings potential. In this case, estimates from the matched-applicant model would be biased upward or downward, depending on whether more talented students chose to matriculate to more or less selective colleges conditional on their options. In the self-revelation model, we compare two students who applied to but were not necessarily accepted by both Penn and Penn State. In this case, the student who attended Penn State is likely to have been rejected by Penn; as a result, the student who attended Penn State is likely to be less promising (as judged by the admissions committee) than the one who attended the University of Pennsylvania. If it is generally true that students with higher unobserved ability are more likely to be accepted by (and therefore more likely to attend) the more selective schools, the self-revelation model is likely to overstate the return to school selectivity. II. DATA AND COMPARISON TO PREVIOUS LITERATURE The College and Beyond (C&B) Survey is described in detail in Bowen and Bok [1998, Appendix A]. The starting point for the database was the institutional records of students who enrolled in (but did not necessarily graduate from) one of 34 colleges in 1951, 1976, and These institutional records were linked to a survey administered by Mathematica Policy Research, Inc. for the Andrew W. Mellon Foundation in and to les provided by the College Entrance Examination Board (CEEB) and the Higher Education Research Institute (HERI) at the University of California, Los Angeles. We focus here on the 1976 entering cohort. While survey data are available for 23,572 stu-

11 ATTENDING A MORE SELECTIVE COLLEGE 1501 dents from this cohort, we exclude students from four historically black colleges and universities. For most of our analysis we restrict the sample to full-time workers, de ned as those who responded yes to the C&B survey question, Were you working full-time for pay or pro t during all of 1995? The 30 colleges and universities in our sample, as well as their average SAT scores and tuition, are listed in Appendix 1. Our nal sample consists of 14,238 full-time, full-year workers. The C&B institutional le consists of information drawn from students applications and transcripts, including variables such as students GPA, major, and SAT scores. These data were collected for all matriculants at the C&B private schools; for the four public universities, however, data were collected for a subsample of students, consisting of all minority students, all varsity letter-winners, all students with combined SAT scores of 1,350 and above, and a random sample of all other students. We constructed weights that equaled the inverse probability of being sampled from each of the C&B schools. Thus, our weighted estimates are representative of the population of students who attend the colleges and universities included in the C&B survey. The C&B institutional data were linked to les provided by HERI and CEEB. The CEEB le contains information from the Student Descriptive Questionnaire (SDQ), which students ll out when they take the SAT exam. We use students responses to the SDQ to determine their high school class rank and parental income. The le that HERI provided is based on data from a questionnaire administered to college freshman by the Cooperative Institutional Research Program (CIRP). We use this le to supplement C&B data on parental occupation and education. Finally, the C&B survey data consist of the responses to a questionnaire that most respondents completed by mail in 1996, although those who did not respond to two different mailings were surveyed over the phone. The survey response rate was approximately 80 percent. The survey data include information on 1995 annual earnings, occupation, demographics, education, civic activities, and satisfaction. 8 Importantly for our purposes, 8. The C&B survey asked respondents to report their 1995 pretax annual earnings in one of the following ten intervals: less than $1,000; $1,000 $9,999; $10,000 $19,999; $20,000 $29,999; $30,000 $49,999; $50,000 $74,999; $75,000 $100,000; $100,000 $149,999; $150,000 $199,999; and more than $200,000. We converted the lowest nine earnings categories to a cardinal scale by assigning values equal to the midpoint of each range, and then calculated the

12 1502 QUARTERLY JOURNAL OF ECONOMICS early in the questionnaire respondents were asked, In rough order of preference, please list the other schools you seriously considered. 9 Respondents were then asked whether they applied to, and were accepted by, each of the schools they listed. 10 By linking the school identi ers to a le provided by HERI, we determined the average SAT score of each school that each student applied to. This information enabled us to form groups of students who applied to a similar set of schools and received the same admissions decisions (i.e., the same combination of acceptances and rejections). Because there were so many colleges to which students applied, we considered schools equivalent if their average SAT score fell into the same 25 point interval. For example, if two schools had an average SAT score between 1200 and 1225, we assumed they used the same admissions cutoff. Then we formed groups of students who applied to, and were accepted and rejected by, equivalent schools. 11 To probe the robustness of our ndings, however, we also present results in which students were matched on the basis of the actual schools they applied to, and on the basis of the colleges Barron s selectivity rating. Table I illustrates how we would construct ve groups of matched applicants for fteen hypothetical students. Students A and B applied to the exact same three schools and were accepted and rejected by the same schools, so they were paired together. The four schools to which students C, D, and E applied were suf ciently close in terms of average SAT scores that they were natural log of earnings. For workers in the topcoded category, we used the 1990 Census (after adjusting the Census data to 1995 dollars) to calculate mean log earnings for college graduates age who earned more than $200,000 per year. The value we assigned for the topcode may be somewhat too low, because income data from the 1990 Census were also topcoded (values of greater than $400,000 on the 1990 Census were recoded as the state median of all values exceeding $400,000) and because students who attended C&B schools may have higher earnings than the population of all college graduates. 9. Students who responded to the C&B pilot survey were not asked this question, and therefore are excluded from our analysis. 10. Students could have responded that they couldn t recall applying or being accepted, as well as yes or no. They were asked to list three colleges other than the one they attended that they seriously considered. In addition, prior to the question on schools the student seriously considered, respondents were asked which school did you most want to attend, that is, what was your rst choice school? If that school was different from the school the student attended, there was a follow-up question that asked whether the student applied to their rst-choice school, and whether they were accepted there. Consequently, information was collected on a maximum of four colleges to which the student could have applied, in addition to the college the student attended. 11. Students who applied to only one school were not included in these matches.

14 1504 QUARTERLY JOURNAL OF ECONOMICS FIGURE I Range of Schools Applied to and Attended by Most Common Sets of Matched Applicants Each bar represents the range of the average SAT scores of the schools that a given set of matched applicants applied to; the shaded area represents the range of schools that students in each set attended. Only matched sets that represent fteen or more students are shown. A total of 3,038 students are represented on the graph. considered to use the same admission standards; because these students received the same admissions decisions from comparable schools, they were categorized as matched applicants. Students were not matched if they applied to only one school (students F and G), or if no other student applied to a set of schools with similar SAT scores (student O). Five dummy variables would be created indicating each of the matched sets. Figure I illustrates the college application and attendance patterns of the most common sets of matched applicants (i.e., those sets that include at least fteen students) in the C&B data set. The length of the bars indicates the range of schools to which

15 ATTENDING A MORE SELECTIVE COLLEGE 1505 each set of matched students applied, and the shaded area of each bar represents the range of schools that each set of students actually attended. The average range of school-average SAT scores of all students who were accepted by at least two schools was 145 points, approximately equal to the spread between Tufts and Yale. If students applied to only a narrow range of schools, then measurement error in the classi cation of school selectivity will be exacerbated in the matched-applicant models. In subsection III.B we present some estimates of the likely impact of this potential bias. Table II provides weighted and unweighted means and standard deviations for individuals who were employed full-time in Everyone in the sample attended a C&B school as a freshman but did not necessarily graduate from the school (or from any school). Nearly 70 percent of students listed at least one other school they applied to in addition to the school they attended. Among students who were accepted by more than one school, 62 percent chose to attend the most selective school to which they were admitted. We were able to match 44 percent of the students with at least one other student in the sample on the basis of the schools that they were accepted and rejected by. Summary statistics are also reported for the subsample of matched applicants. It is clear that the schools in the C&B sample are very selective. The students average SAT score (Math plus Verbal) exceeds 1,100. Over 40 percent of the sample graduated in the top 10 percent of their high school class. The mean annual earnings in 1995 for full-time, full-year workers was $84,219, which is high even for college graduates. Because the C&B data set represents a restricted sample of elite schools and is not nationally representative, we compared the payoff to attending a more selective school in the C&B sample to corresponding OLS estimates from national samples. When we replicated the wage regressions based on the High School and Beyond Survey in Kane [1998] and the National Longitudinal Survey of Youth in Daniel, Black, and Smith [1997], we found that OLS estimates of the return to college selectivity based on the C&B survey were not signi cantly distinguishable from, though slightly higher than, those from these nationally representative data sets (see Dale and Krueger [1999]). In the next section we examine whether estimates of this type are confounded by unobserved student attributes.

16 1506 QUARTERLY JOURNAL OF ECONOMICS TABLE II MEANS AND STANDARD DEVIATIONS OF THE C&B DATA SET Unweighted Weighted* Full sample Full sample Matched applicants Variable Mean Standard deviation Mean Standard deviation Mean Standard deviation Log(earnings) Annual earnings (1995 dollars) 86,768 62,504 84,219 60,841 88,276 62,598 Female Black Hispanic Asian Other race Predicted log (parental income) Own SAT/ School average SAT/ Net tuition (1976 dollars) Log(net tuition) High school top 10 percent High school rank missing College athlete Average SAT/100 of schools applied to One additional application Two additional applications Three additional applications Four additional applications Undergraduate percentile rank in class Attained advanced degree Graduated from college Public college Private college Liberal arts college N 14,238 14,238 6,335 * Means are weighted to make the sample representative of the population of students at the C&B institutions. III. THE EFFECT OF COLLEGE SELECTIVITY AND OTHER CHARACTERISTICS ON EARNINGS Table III presents our main set of log earnings regressions. We limit the sample to full-time, full-year workers, and estimate

17 ATTENDING A MORE SELECTIVE COLLEGE 1507 TABLE III LOG EARNINGS REGRESSIONS USING COLLEGE AND BEYOND SURVEY, SAMPLE OF MALE AND FEMALE FULL-TIME WORKERS Model Basic model: no selection controls Full sample Restricted sample Matchedapplicant model Similar school- SAT matches* Alternative matched-applicant models Exact school- SAT matches** Barron s matches*** Selfrevelation model Variable School-average SAT score/100 (0.016) (0.014) (0.022) (0.036) (0.016) (0.018) Predicted log(parental income) (0.024) (0.033) (0.033) (0.079) (0.028) (0.025) Own SAT score/ (0.006) (0.007) (0.007) (0.014) (0.005) (0.006) Female (0.015) (0.018) (0.024) (0.049) (0.017) (0.014) Black (0.035) (0.053) (0.053) (0.049) (0.039) (0.035) Hispanic (0.052) (0.076) (0.099) (0.206) (0.066) (0.053) Asian (0.036) (0.054) (0.064) (0.141) (0.049) (0.037) Other/missing race (0.119) (0.143) (0.180) (0.083) (0.134) (0.116) High school top percent (0.018) (0.022) (0.026) (0.032) (0.024) (0.019) High school rank missing (0.024) (0.026) (0.038) (0.066) (0.027) (0.022) Athlete (0.025) (0.030) (0.039) (0.096) (0.033) (0.024) Average SAT score/ 100 of schools applied to One additional application Two additional applications Three additional applications Four additional applications (0.013) (0.011) (0.022) (0.028) (0.027) Adjusted R N 14,238 6,335 6,335 2,330 9,202 14,238 Each equation also includes a constant term. Standard errors are in parentheses and are robust to correlated errors among students who attended the same institution. Equations are estimated by WLS and are weighted to make the sample representative of the population of students at the C&B institutions. * Applicants are matched by the average SAT score (within 25 point intervals) of each school at which they were accepted or rejected. This model includes 1,232 dummy variables representing each set of matched applicants. ** Applicants are matched by the average SAT score of each school at which they were accepted or rejected. This model includes 654 dummy variables representing each set of matched applicants. *** Applicants are matched by the Barron s category of each school at which they were accepted or rejected. This model includes 350 dummy variables representing each set of matched applicants.

18 1508 QUARTERLY JOURNAL OF ECONOMICS separate Weighted Least Squares (WLS) regressions for a pooled sample of men and women. 12 The reported standard errors are robust to correlation in the errors among students who attended the same college. With the exception of a dummy variable indicating whether the student participated on a varsity athletic team, the explanatory variables are all determined prior to the time the student entered college. Most of the covariates are fairly standard, although an explanation of predicted log parental income is necessary. Parental income was missing for many individuals in the sample. Consequently, we predicted income by rst regressing log parental income on mother s and father s education and occupation for the subset of students with available family income data, and then multiplied the coef cients from this regression by the values of these explanatory variables for every student in the sample to derive the regressor used in Table III. The basic model, reported in the rst column of Table III, is comparable to the models estimated in much of the previous literature in that no attempt is made to adjust for selective admissions beyond controlling for variables such as the student s own SAT score and high school rank. This model indicates that students who attended a school with a 100 point higher average SAT score earned about 7.6 percent higher earnings in 1995, holding constant their own SAT score, race, gender, parental income, athletic status, and high school rank. Column 2 also presents results of the basic model, but restricts the sample to those who are included in the matchedapplicants subsample. As mentioned earlier, we formed groups of matched applicants by treating schools with average SAT scores in the same 25 point range as equally selective. We were able to match only 6,335 students with at least one other student who applied to, and was accepted and rejected by, an equivalent set of institutions. As shown in column 2 of Table III, when we estimate the basic model using this subsample of matched applicants, we obtain results very similar to those from the full sample. When we include dummies indicating the sets of matched applicants in column 3, however, the effect of school-average SAT is slightly negative and statistically indistinguishable from zero. Although the standard error doubles when we look within 12. The sample of women was too small to draw precise estimates from, but the results were qualitatively similar. The results for men were also similar and more precisely estimated (see Dale and Krueger [1999]).

19 ATTENDING A MORE SELECTIVE COLLEGE 1509 matched sets of students, we can reject an effect of around 3 percent higher earnings for a 100 point increase in the schoolaverage SAT score; that is, we can reject an effect size that is at the low end of the range found in the previous literature. Column 4 of Table III presents results from an alternative version of the matched-applicant model that uses exact matches that is, students who applied to and were accepted or rejected by exactly the same schools. When we estimate a xed effects model for the 2,330 students we could exactly match with other students, the relationship between school-average SAT score and earnings is negative and statistically signi cant. Thus, the cruder nature of the previous matches does not appear to be responsible for our results. To increase the sample and improve the precision of the estimates, we also used the selectivity categories from the 1978 edition of Barron s Guide as an alternative way to match students. Barron s is a well-known and widely used measure of school selectivity. Speci cally, we classi ed the schools students applied to according to the following Barron s ratings: (1) Most Competitive, (2) Highly Competitive, (3) Very Competitive, and (4) a composite category that included Competitive, Less Competitive, and Non-Competitive. Then we grouped students together who applied to and were accepted by a set of colleges that were equivalent in terms of the colleges Barron s ratings. This generated a sample of 9,202 matched applicants. As shown in Column 5 of Table III, when we estimated a xed effects model for this sample the coef cient on the school-average SAT score was 0.004, with a standard error of In short, the effect of school-sat score was not signi cantly greater than zero in any version of the matched-applicant model that we estimated. Results of the self-revelation model are shown in column 6 of Table III. This model includes the average SAT score of the schools to which students applied and dummy variables indicating the number of schools to which students applied to control for selection bias. The effect of the school-average SAT score in these models is close to zero and more precisely estimated than in the matched-applicant models. 13 Because the self-revelation model is 13. Because the C&B earnings data are topcoded, we also estimated Tobit models. Results from these models were qualitatively similar to our WLS results. When we estimated a Tobit model without selection controls (similar to our basic model), the coef cient (standard error) on school SAT score was.083 (.008); the coef cient on school-sat score falls to (.012) if we also control for the variables in our self-revelation model.

20 1510 QUARTERLY JOURNAL OF ECONOMICS TABLE IV THE EFFECT OF SCHOOL-AVERAGE SAT SCORE ON EARNINGS IN MODELS THAT USE ALTERNATIVE SELECTION CONTROLS, C&B SAMPLE OF MALE AND FEMALE FULL-TIME WORKERS Parameter estimates Type of selection control School-average SAT score Selection control N (1) None (basic model) ,238 (0.016) (2) Average SAT score/100 of schools ,238 applied to (self-revelation model) (0.018) (0.013) (3) Average SAT score/100 of schools ,238 accepted by (0.021) (0.017) (4) Highest SAT score/100 of schools ,238 accepted by (0.018) (0.021) (5) Highest SAT score/100 of all ,238 schools applied to (0.015) (0.013) (6) Highest SAT score/100 of schools ,358 applied to but not attended (0.013) (0.006) (7) Average SAT score/100 of schools ,805 rejected by (0.015) (0.012) (8) Highest SAT score/100 of schools accepted by not attended ,257 (0.014) (0.010) Each model also includes the same control variables as the self-revelation model shown in column 3 of Table III. Standard errors are in parentheses and are robust to correlated errors among students who attended the same institution. Equations are estimated by WLS and are weighted to make the sample representative of the population of students at the C&B institutions. The rst data column presents the coef cient on the average SAT score at the school the student attended; the second data column presents the coef cient on the selection control described in the left margin of the table. likely to undercorrect for omitted variable bias, the fact that the results of this model are so similar to the matched-applicant models is reassuring. Table IV presents parameter estimates from models that are similar to the self-revelation model, but use alternative selection controls in place of the average SAT score of the schools to which the student applied. 14 For example, the third row reports esti- 14. Each of these models also includes dummy variables representing the number of colleges the student applied to, because the number of applications a student submits may reveal his unobserved ability. However, even if we exclude the application dummies from the self-revelation model, the return to college average SAT-score is not signi cantly different from zero; in this model, the coef cient (standard error) on school-sat score is.017 (.017).

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